Technical Field
The present disclosure relates to a pedestrian detecting system. More particularly, the present disclosure relates to a pedestrian detecting system applicable to an insufficiently-illuminated environment.
Description of Related Art
With a daily increasing complicated driving environment, safety requirements for driving are increasing. Many manufacturers have been devoted to developing an intelligent driving system. In addition to detecting surrounding environment, the intelligent driving system further needs to detect objects (e.g. a pedestrian or a vehicle) on the road, thereby enabling a driver to react instantly to changes of the surrounding environment.
However, when a driver is driving on the road, the pedestrian is an object that is most needed to be attended but is very difficult to be detected because the pedestrian's stance, clothes color, size and shape all have complicated changes. In order to accurately detect the pedestrian, an appearance feature and an objectness feature have to be taken in consideration. An image is generally shown with a two-dimensional plane, and the objectness feature is obtained by analyzing the appearance feature of the entire two-dimensional image. A scene at which the pedestrian located is very likely to be very complicated, which not only includes information of one single pedestrian, but also includes information of depth. For example, another pedestrian or object may be located in front of the pedestrian or behind the pedestrian, and the foreground and background involving depth information in the scene of the two-dimensional image are generally processed by similar methods. The compound foreground and background result in inaccurately detecting the pedestrian in the two-dimensional image.
For overcoming the aforementioned problem, a conventional skill uses a depth sensor such as radar. The depth sensor is used to detect a distance between the pedestrian and the vehicle in order to determine if a real pedestrian exists. However, the depth sensor merely can be used to detect the distance, but fails to detect the appearance of the pedestrian, and thus cannot accurately detect out the pedestrian.
Furthermore, the conventional pedestrian detecting system is limited to processing environments with similar luminance intensities, but is not applicable to an environment with a high contrast. For example, when a Support Vector Machine using a Histogram of Oriented Gradient and a local area vector as a combined feature, the target object recognized thereby is limited to a day-time pedestrian whose appearance is similar to data of a training model, and it is difficult to handle a target object located under an insufficiently-illuminated environment. Furthermore, the training model is mainly focused on a trained object considering the entire image, and is not suitable for recognizing the target object which only has partial ideal image regions due to the high contrast.